{"id":"https://openalex.org/W4402353262","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651546","title":"Periodicity Association Based Contrastive Learning for Time Series Anomaly Detection","display_name":"Periodicity Association Based Contrastive Learning for Time Series Anomaly Detection","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402353262","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651546"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651546","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10651546","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027059074","display_name":"Jiawei Xu","orcid":"https://orcid.org/0009-0005-2531-3454"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Xu","raw_affiliation_strings":["Central South University,Sch. of Comp. Sci. & Eng.,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Central South University,Sch. of Comp. Sci. & Eng.,Changsha,China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025441627","display_name":"Zhaowei Zhu","orcid":"https://orcid.org/0000-0003-3894-5862"},"institutions":[{"id":"https://openalex.org/I1328775524","display_name":"Zhejiang Sci-Tech University","ror":"https://ror.org/03893we55","country_code":"CN","type":"education","lineage":["https://openalex.org/I1328775524"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaowei Zhu","raw_affiliation_strings":["Zhejiang Sci-Tech University,Sch. of Comp. Sci. & Tech.,Hangzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Sci-Tech University,Sch. of Comp. Sci. & Tech.,Hangzhou,China","institution_ids":["https://openalex.org/I1328775524"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087340305","display_name":"Weiwei Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Ye","raw_affiliation_strings":["Central South University,Sch. of Comp. Sci. & Eng.,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Central South University,Sch. of Comp. Sci. & Eng.,Changsha,China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012277801","display_name":"Ning Gui","orcid":"https://orcid.org/0000-0003-4983-5327"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ning Gui","raw_affiliation_strings":["Central South University,Sch. of Comp. Sci. & Eng.,Changsha,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Central South University,Sch. of Comp. Sci. & Eng.,Changsha,China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3556,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.5443893,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.994700014591217,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7267816066741943},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6739266514778137},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6612979173660278},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.637940526008606},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5461427569389343},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5127790570259094},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.471378892660141},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.4475117027759552},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33463066816329956},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10757437348365784},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09713748097419739}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7267816066741943},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6739266514778137},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6612979173660278},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.637940526008606},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5461427569389343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5127790570259094},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.471378892660141},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.4475117027759552},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33463066816329956},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10757437348365784},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09713748097419739},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651546","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn60899.2024.10651546","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1970088130","https://openalex.org/W2132870739","https://openalex.org/W2296719434","https://openalex.org/W2407991977","https://openalex.org/W2535642622","https://openalex.org/W2593048559","https://openalex.org/W2785362611","https://openalex.org/W2786827964","https://openalex.org/W2950361482","https://openalex.org/W2954731415","https://openalex.org/W2963166639","https://openalex.org/W2965433388","https://openalex.org/W2971121529","https://openalex.org/W3004207920","https://openalex.org/W3036224891","https://openalex.org/W3093787659","https://openalex.org/W3098957257","https://openalex.org/W3099971460","https://openalex.org/W3106543020","https://openalex.org/W3114632476","https://openalex.org/W3169661950","https://openalex.org/W3170937175","https://openalex.org/W3170981104","https://openalex.org/W3171007011","https://openalex.org/W3190152617","https://openalex.org/W3199148273","https://openalex.org/W3204263062","https://openalex.org/W4242856212","https://openalex.org/W4254182148","https://openalex.org/W4287393709","https://openalex.org/W4320013936","https://openalex.org/W4387447989","https://openalex.org/W4393714504","https://openalex.org/W6631190155","https://openalex.org/W6745092361","https://openalex.org/W6748102297","https://openalex.org/W6751494907","https://openalex.org/W6751580242","https://openalex.org/W6759891849","https://openalex.org/W6764679822","https://openalex.org/W6774314701","https://openalex.org/W6779997284","https://openalex.org/W6784869275","https://openalex.org/W6787725836","https://openalex.org/W6788705105","https://openalex.org/W6789805323","https://openalex.org/W6798224550","https://openalex.org/W6802061597","https://openalex.org/W6810637551","https://openalex.org/W6845625448"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W2392697706","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W366033468","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928"],"abstract_inverted_index":{"Unsupervised":[0],"timeseries":[1],"anomaly":[2,78],"detection":[3],"(UTAD)":[4],"aims":[5],"to":[6,67,118,149,166],"identify":[7],"abnormal":[8],"patterns":[9,73],"within":[10,124],"time":[11,46,97],"series":[12,47,98],"data":[13],"and":[14,51,106,129,153,161],"is":[15],"of":[16,102],"immense":[17],"importance":[18],"in":[19,39,110],"extensive":[20],"applications.":[21],"Contrastive":[22],"learning":[23,65,144],"has":[24],"been":[25],"seen":[26],"as":[27,33,44,99,167],"an":[28,100],"effective":[29],"candidate":[30],"for":[31,77,92,107],"UTAD":[32],"it":[34],"can":[35],"learn":[36,68],"invariants":[37],"existing":[38],"two":[40,147],"contrastive":[41,64,90,143],"views.":[42],"However,":[43],"most":[45],"contain":[48],"complex":[49],"multi-periodic":[50],"non-periodic":[52],"signals,":[53],"the":[54,111,125,132,146,181],"huge":[55],"difference":[56],"between":[57,145,158],"sequences":[58],"with":[59,122],"different":[60,136],"periodicity":[61,88],"would":[62],"make":[63],"hard":[66],"representative":[69],"temporal":[70,151],"and/or":[71],"spatial":[72],"that":[74,174],"are":[75],"essential":[76],"detection.":[79],"To":[80],"address":[81],"this":[82],"issue,":[83],"we":[84,95,114,164],"propose":[85],"PACdetector,":[86],"a":[87,155],"association-based":[89],"framework":[91],"UTAD.":[93],"Specifically,":[94],"perceive":[96],"aggregation":[101],"various":[103,177],"periodic":[104],"sequences,":[105,113],"each":[108],"point":[109],"periodical":[112],"employ":[115],"self-attention":[116],"maps":[117],"calculate":[119],"its":[120],"association":[121],"points":[123,130,160],"period":[126],"(intraperiod":[127],"association)":[128],"at":[131],"same":[133],"phase":[134],"across":[135,184],"periods":[137],"(interperiod":[138],"association).":[139],"We":[140],"then":[141],"perform":[142],"associations":[148],"preserve":[150],"consistency":[152],"obtain":[154],"distinguishable":[156],"criterion":[157],"normal":[159],"anomalies,":[162],"which":[163],"refer":[165],"Periodicity":[168],"Association":[169],"Discrepancy.":[170],"Extensive":[171],"experiments":[172],"show":[173],"PACdetector":[175],"outperforms":[176],"state-of-the-art":[178],"algorithms,":[179],"achieving":[180],"best":[182],"performance":[183],"six":[185],"benchmark":[186],"datasets.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}